Scenario 3: Multi-Agent Legislative Impact Analysis Platform

Core Logic

The Orchestrator Agent coordinates the entire legislative analysis workflow using a supervisor pattern. It validates input configuration and creates workflow plans estimating completion time based on enabled features. The agent delegates tasks to specialized agents in proper sequence respecting dependencies (Document Analyzer before Client Matcher, Risk Assessor before Financial Calculator). It monitors progress across all workflow steps, manages inter-agent communication routing messages between agents, ensures quality control by validating agent outputs before handoffs, handles errors with appropriate retry logic and fallback strategies, and compiles final output aggregating results from all agents into comprehensive analysis. Reasoning traces demonstrate: receiving analysis request with configuration details, creating workflow plan with estimated completion, and coordinating step-by-step execution. The Orchestrator maintains workflow metrics including total duration, LLM calls, tool calls, RAG queries, tokens used, agents involved, success rate, and error count.

Core Logic

The Knowledge Retriever Agent performs semantic vector search across configured knowledge sources using embedding models. It generates query embeddings from analysis context, searches vector database (simulating Pinecone) with configurable topK results and similarity threshold, applies reranking to optimize relevance ordering, and returns retrieved chunks with full metadata. Each chunk includes content, source name and type (legislation, memento, case_law, client_data), relevance score, and metadata (title, date, author, section, page number). Highlights show matching text for quick verification. The agent tracks search metrics: query embedding time, search time, rerank time, total time, documents scanned, and chunks returned. Reasoning traces show: initiating vector search across configured sources with query preview, and concluding with result count, top sources, and average relevance percentage.

Core Logic

The Document Analyzer Agent performs comprehensive document analysis using NLP. It extracts named entities including law references (Article 35, 35.1, 35.4), dates (April 1, 2025), amounts (EUR 10,000,000, EUR 500K, EUR 1M), percentages (42%, 35%, 25%), and affected parties with confidence scores. The agent classifies document categories (Tax Law, R&D Incentives) with supporting keywords. It identifies key provisions with section references, titles, summaries, impact ratings (HIGH/MEDIUM/LOW), and effective dates. The agent calculates aggregate impact metrics: rate reduction percentages, affected population estimates, annual financial impact. Tool execution uses analyze_document with comprehensive analysis depth, producing structured output including entities, classifications, key provisions with section references, and severity classification. Reasoning traces demonstrate: observing document details and modification targets, extracting entities with specific values, analyzing impact severity with calculations, and concluding with classification, affected count, and aggregate impact.

Core Logic

The Client Matcher Agent applies Multi-Criteria Decision Analysis (MCDA) scoring to systematically prioritize affected clients. It queries the client database filtering by relevant practice areas and exposure criteria. The agent applies weighted scoring criteria: Practice Area Alignment (30%) measuring how closely the legislation relates to the client's primary practice areas, Financial Exposure (25%) calculating potential financial impact based on client's current credits, projects, and revenue, Operational Impact (20%) assessing process changes required, Compliance Urgency (15%) evaluating deadline proximity and compliance gap severity, and Relationship Value (10%) considering strategic importance of the client relationship. For each matched client, the agent generates a detailed analysis including name, priority rating (HIGH/MEDIUM/LOW), calculated financial impact, required actions count, compliance deadline, and composite risk score. Reasoning traces show: accessing client database with filter criteria, applying MCDA scoring with weighted criteria, performing detailed client analysis with specific calculations, and concluding with prioritized list and total portfolio impact.

Core Logic

The Advisory Writer Agent generates personalized client advisories using context from all previous agents. It accesses client profiles and historical data (prior year tax filings, project portfolios) for personalization. Each advisory follows a structured format: Executive Summary with headline impact, Background explaining the regulatory context, What Changed detailing specific modifications, Client-Specific Impact with personalized financial calculations, Required Actions with specific deadlines, Risk Assessment with probability-weighted outcomes, and Next Steps for engagement. The agent incorporates calculated financial impact (rate reduction impact, certification costs, process implementation costs), specific article references with accurate citations, deadline-based action items with responsible parties, and risk-adjusted recommendations. Quality validation ensures accuracy percentage, personalization score, completeness rating, legal citation verification, and readability assessment (Executive level). Tool execution uses generate_advisory with client IDs, legislation reference, and template selection. Reasoning traces demonstrate: observing advisory requirements and accessing personalization data, planning advisory structure, drafting with specific financial calculations, and reflecting on quality validation results.

Core Logic

The Compliance Checker Agent analyzes compliance requirements from new legislation against current client practices. It identifies specific compliance gaps with structured analysis: area affected, current state description, required state per new regulation, gap characterization, severity rating (CRITICAL/HIGH/MEDIUM/LOW), and recommended remediation. The agent calculates aggregate compliance metrics including overall compliance score, critical gap count, and high gap count. It assesses process changes required with current vs new process descriptions, impacted teams, and implementation effort rating. Tool execution uses validate_compliance with legislation ID and comprehensive check type. Reasoning traces show: observing analysis scope against current practices, identifying specific gaps with detail on current vs required states, and concluding with gap summary, remediation effort assessment, and estimated compliance cost per client.

Core Logic

The Risk Assessor Agent performs deep risk analysis using multi-factor scoring across categories: Compliance Risk (30%), Financial Exposure (25%), Operational Impact (20%), Regulatory Scrutiny (15%), and Reputational Risk (10%). It integrates historical enforcement data (e.g., AEAT audit patterns) to inform probability assessments. Each risk factor is evaluated with severity score (1-10), likelihood score (1-10), composite risk score, and specific indicators. The agent generates mitigation strategies with effectiveness score, implementation cost, time to implement, priority level, and owner assignment. Penalty predictions include type (fine, sanction, license revocation, audit, litigation), probability, amount range with currency, and regulatory basis. Scenario analysis models three outcomes: best case (proactive compliance), expected (reactive adaptation), and worst case (audit with penalties), each with financial impact, probability, description, and key assumptions. Agent collaboration requests validation from Compliance Checker for risk findings. Tool execution uses assess_risk_score with legislation ID, client portfolio, and scenario inclusion flag. Reasoning traces demonstrate: initiating risk assessment observing analysis scope, applying multi-factor risk scoring with criteria, generating scenario analysis with probability weighting, cross-referencing enforcement data for patterns, and concluding with portfolio score, critical/high zone counts, and total penalty exposure.

Core Logic

The Financial Calculator Agent computes comprehensive financial impact analysis. It calculates impact by category including direct costs (rate reduction, certification), indirect costs (process changes, training), opportunity costs (delayed credits, missed optimization), and potential savings (efficiency gains, penalty avoidance), producing net impact with confidence level for each category. Cost-benefit analysis generates total costs, total benefits, net benefit, break-even point, payback period, and recommendation. Cash flow projections show period-by-period inflow, outflow, net flow, and cumulative flow. Sensitivity analysis tests key variables with base case, optimistic, and pessimistic scenarios. Tool execution uses financial analysis tools with client IDs and scenario inclusion. Reasoning traces demonstrate: observing analysis scope with data retrieval, planning financial model structure, performing client-specific calculations, and concluding with total impact and recommendation.

Core Logic

The Quality Reviewer Agent performs comprehensive quality validation across all analysis outputs. It checks accuracy by cross-referencing legal citations against source documents, validating financial calculations, and verifying client impact assessments. Completeness validation ensures all required sections are present and sufficiently detailed. Consistency checking identifies contradictions or discrepancies across outputs from different agents. Regulatory alignment verification confirms all compliance references are accurate and current. When issues are detected, the agent initiates self-correction loops: identifying the issue, determining correction approach, applying the fix, and re-validating. Corrections are tracked with original value, corrected value, correction reason, and iteration count. Quality metrics include overall score, accuracy score, completeness score, consistency score, regulatory alignment score, review iterations, and human verification needed flag. Corrections applied are documented with type, location, original/corrected values, and reason. Validation checks detail each check performed with pass/fail status and score. Tool execution uses validate_quality with accuracy, completeness, and consistency flags. Reasoning traces demonstrate: observing quality review initiation with module count, performing accuracy checks with verification counts, reflecting on detected issues and initiating self-correction, applying corrections with recalculations, and concluding with overall score, sub-scores, corrections count, and approval status.